The effect of financial knowledge, financial behavior and digital financial capabilities on financial inclusion, financial concern and performance in MSMEs in East Java
Bibliographic record
Abstract
This study aims to prove and analyze the effect of financial knowledge, financial behavior, and digital financial capabilities on financial inclusion, financial concern, and performance in Small and Medium Enterprises (SMEs) in East Java. The population used in this study was 1,387,854 Micro, Small and Medium sized Enterprises (MSMEs) actors in East Java, which is located in the Gerbangk ertasusila area. The sample in this study were 395 respondents who were determined by the non-probability sampling method. In this study a questionnaire research instrument was used, namely a set of questions answered to respondents to obtain written information related to research variables and used the Structural Equation Modeling (SEM) analysis technique. The results of the study show that: (1) Financial knowledge has a significant effect on financial inclusion, (2) Financial behavior has a significant effect on financial inclusion, (3) Digital financial capability has a significant effect on financial inclusion, (4) ) Financial inclusion has no significant effect on financial problems, (5) Financial knowledge has a significant effect on financial concerns, (6) Financial behavior has a positive and significant effect on financial concerns, (7) Digital financial capability has a significant effect on financial concern, (8) Financial knowledge has an insignificant effect on MSME performance, (9) Financial Behavior has no significant effect on MSME performance, (10) Digital financial capability has no significant effect on MSME performance, (11) Financial inclusion has a positive and significant effect on MSME performance. (12) Financial concern has a positive and significant effect on MSME performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".